Models, Simulation, Product Development and Testing

Why Models and Simulation Matter in Product Development

I was recently at an event at an Advanced Manufacturing event at a local technical school. There was a superb presentation on a student project; nothing like real-world applications to uncover the things presented to the development team. We can contrive things, but the actual work environment, product, and project challenges, well, let’s say, often go beyond our ability to imagine, usually a combination of emerging events that cause us some consternation. They presented their design approach, and ultimately, the product will be used, so this was not a theoretical exercise. When asked how they would qualify the design, they answered via models and high-end simulation.

This is the difference between the real world and theoretical education. In the real world, the result of relying solely on simulation to verify the product will likely result in learning some essential things very late in the product development cycle. Models may or may not reflect the real world; it is incumbent upon the team to consider the risk (veracity) in the models, ideally testing the prototype and comparing the outcome to the model predictions. Engineers and designers use these technologies to predict real-world performance, identify potential failures, and refine prototypes before physical testing. By leveraging computational models, companies can reduce development costs, accelerate time to market, and improve product reliability.

Connection Between Models, Simulation, and Real-Life Testing

A common question in product development is: How well do models and simulation represent real-life use in the product’s environment? The answer depends on how accurately the virtual model captures the product’s physical characteristics and the parameters to which the product will be subjected in the operating environment – for example, vibration, thermal, and torque loading that may not be well understood. Here’s a breakdown of how these approaches align:

1. Predicting Performance

Simulation models use physics-based equations and real-world data to predict behavior under different conditions. This helps engineers foresee product performance without the need for extensive prototypes. However, variations in material properties, environmental factors, and unforeseen interactions may lead to discrepancies between simulations and actual results. To create the models, we will need to understand that working environment; we can use documents such as SAE International J Standards. These are helpful, but not a panacea – engineers and their experience or measurements generate these standards but may not comprehensively represent the application.

2. Cost Reduction and Efficiency

Physical prototypes are expensive and time-consuming. Models and simulation allow developers to test multiple design variations virtually, optimizing structures, aerodynamics, and durability before committing to production. This includes what materials we can use for the design solution. When simulations match real-world testing results, companies save significant time and resources.

3. Risk Management and Failure Prediction

Product teams can identify potential weaknesses by running thousands of simulations under different stress conditions. Not only thousands of simulations on a single product, but on multiple design incarnations to meet the customer and business objectives. In product development, it is not always (maybe almost never) a good idea to select a single solution and put all of our eggs in that basket from the start. This proactive approach minimizes the chances of costly recalls and failures, ensuring the final product meets safety and regulatory requirements.

Comparing Model and Simulation Outcomes to Real-World Performance

While models and simulation can provide high accuracy, there are limitations:

Material Variations: The behavior of real-world materials may differ slightly from theoretical models, especially if we do not include the full range of material performance possibilities – not average.
Environmental Factors: Unexpected variables such as temperature fluctuations, humidity, and unforeseen stresses can impact real-life results. We often have less than perfect knowledge about the product operating space.
Software and Computational Constraints: Simulation models are only as good as the assumptions and data fed into them. Inaccurate inputs can lead to misleading results.

To bridge the gap, companies integrate models and simulation with real-world testing through iterative validation—adjusting virtual models based on experimental data. The sequence:

Build the model
Perform simulation
Build a representative prototype
Compare test results to simulation
Identify non-conformances
Update the model
Go to step 1

Conclusion: Integrating Models, Simulation, and Testing for Success

The synergy between models, simulation, and real-world testing drives quality and sometimes innovation in product development. By validating simulation results with actual testing, companies can create high-performing, high-quality cost-effective, and reliable products. As technology advances, digital twins and AI-driven simulations will further enhance predictive capabilities, making product development more precise.